Fact and Dimension Table

1. Introduction

In data warehousing, data is structured into Fact Tables and Dimension Tables to facilitate efficient querying and analysis. This structure follows the Star Schema or Snowflake Schema, enabling faster reporting and analytics.

2. Fact Table

A Fact Table stores business transactions or events and contains numeric measures (also called facts) along with foreign keys linking to dimension tables.

Key Characteristics of a Fact Table:

1. Contains Numerical Data (Measures): These are the quantitative metrics used for analysis (e.g., sales amount, revenue, quantity sold).
2. Foreign Keys to Dimension Tables: Fact tables have foreign key relationships with multiple dimension tables.
3. Granularity: Represents the level of detail in the data (e.g., per transaction, daily, monthly).
4. Supports Aggregation: Can be aggregated for reporting (e.g., total sales per month, region).

Types of Fact Tables

1. Transactional Fact Table: Stores detailed transactions (e.g., a record per sales transaction).
2. Aggregated Fact Table: Stores pre-aggregated data to improve performance.
3. Snapshot Fact Table: Stores periodic snapshots (e.g., daily stock levels).
4. Factless Fact Table: Contains only keys without measures (e.g., student course enrollment data).

3. Dimension Table

A Dimension Table stores descriptive, textual, or categorical data that provides context for facts. They help slice and dice the numerical data in meaningful ways.

Key Characteristics of a Dimension Table:

1. Contains Descriptive Attributes: Stores textual information (e.g., customer name, product name, store location).
2. Primary Key: Each dimension table has a unique identifier (Surrogate Key).
3. Supports Hierarchies: Dimensions help in hierarchy-based analysis (e.g., Year → Month → Day for time dimensions).
4. Denormalized for Performance: Dimension tables are usually wide and not deeply normalized.

Types of Dimension Tables

1. Conformed Dimensions: Shared across multiple fact tables (e.g., Date Dimension used in Sales and Inventory Fact Tables).
2. Junk Dimensions: Stores unrelated attributes (e.g., payment method, discount flags).
3. Slowly Changing Dimensions (SCDs): Handles changes over time (e.g., address change for a customer). Type 1: Overwrites old data. Type 2: Maintains history by adding a new row. Type 3: Maintains limited history using extra columns.

4. Star Schema vs. Snowflake Schema

1. Star Schema

• Fact table is in the center, surrounded by denormalized dimension tables.
• Faster query performance since joins are minimized.
• Best for reporting and analytics.

Example:

           Date_Dim
               |
Product_Dim -- Sales_Fact -- Customer_Dim
               |
           Store_Dim

        

2. Snowflake Schema

• Dimension tables are normalized (split into multiple related tables).
• Reduces redundancy but increases query complexity due to joins.

Example:

           Date_Dim
               |
Product_SubCategory_Dim -- Product_Dim -- Sales_Fact -- Customer_Dim
               |
           Store_Dim

        

5. Example Query

Question: Find total sales per product for March 2024.

SELECT
    p.Product_Name,
    SUM(f.Sales_Amount) AS Total_Sales
FROM Sales_Fact f
JOIN Product_Dim p ON f.Product_ID = p.Product_ID
JOIN Date_Dim d ON f.Date_ID = d.Date_ID
WHERE d.Month = 'March' AND d.Year = 2024
GROUP BY p.Product_Name;

        

7. Real-World Examples

Retail (Amazon, Walmart)

• Fact Table: Sales transactions with product, customer, store, and date references.
• Dimension Tables: Product details, customer demographics, store locations.

Finance (Banking, Insurance)

• Fact Table: Transactions, withdrawals, deposits.
• Dimension Tables: Account type, customer details, branch locations.

Healthcare (Hospital Data)

• Fact Table: Patient visits, treatments.
• Dimension Tables: Patient details, doctor details, hospital department.

8. Conclusion

• Fact Tables store measurable transactional data (e.g., sales, revenue).
• Dimension Tables store descriptive attributes (e.g., product name, customer details).
• Star Schema is used for faster query performance, whereas Snowflake Schema reduces data redundancy.
• These structures power business intelligence (BI) and analytics.

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